Fuzzy sets and fuzzy rough sets are widely applied in data analysis, data mining, and
decision-making. So far, the common method is to use rough approximate operators to …

This paper addresses a foundational aspect of imprecision in information and knowledge. It
makes a convincing case that convexity can take part in the progress of rough set theory in …

Covering granular reduction is an important issue in multi-covering information systems. The
main methods to solve this problem are set operators. How to solve this problem by …

Rough sets provide an efficient tool for attribute reduction and rule extraction. However,
many important problems in rough set theory, including attribute reduction, are NP-hard and …

Rough sets are efficient for data pre-processing in data mining. However, some important
problems such as attribute reduction in rough sets are NP-hard, and the algorithms to solve …

The matroid concept is an important model in real life applications. Determining the
existence of mutations of DNA and RNA plays an essential role in biological studies. The …

Rough set theory has been successfully applied to vague and uncertain data due to its
approximation ability. Matroid is a sophisticated mathematical structure to provide a unifying …

Coverings are a useful form of data, while covering-based rough sets provide an effective
tool for dealing with this data. Covering-based rough sets have been widely used in attribute …

Considerable progress has been made in the theory of covering-based rough sets.
However, there has been a lack of research on their application to classification tasks …

In covering-based rough sets, many basic issues are related to minimal and maximal
descriptions. For a covering approximation space with a large cardinal, it would be tedious …